Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, signal interference). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then, it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area. IEE

Arabidopsis wat1 (walls are thin1)-mediated resistance to the bacterial vascular pathogen, Ralstonia solanacearum, is accompanied by cross-regulation of salicylic acid and tryptophan metabolism

Inactivation of Arabidopsis WAT1 (Walls Are Thin1), a gene required for secondary cell-wall deposition, conferred broad-spectrum resistance to vascular pathogens, including the bacteria Ralstonia solanacearum and Xanthomonas campestris pv. campestris, and the fungi Verticillium dahliae and Verticillium albo-atrum. Introduction of NahG, the bacterial salicylic acid (SA)-degrading salicylate hydroxylase gene, into the wat1 mutant restored full susceptibility to both R. solanacearum and X. campestris pv. campestris. Moreover, SA content was constitutively higher in wat1 roots, further supporting a role for SA in wat1-mediated resistance to vascular pathogens. By combining transcriptomic and metabolomic data, we demonstrated a general repression of indole metabolism in wat1-1 roots as shown by constitutive down-regulation of several genes encoding proteins of the indole glucosinolate biosynthetic pathway and reduced amounts of tryptophan (Trp), indole-3-acetic acid and neoglucobrassicin, the major form of indole glucosinolate in roots. Furthermore, the susceptibility of the wat1 mutant to R. solanacearum was partially restored when crossed with either the trp5 mutant, an over-accumulator of Trp, or Pro35S:AFB1-myc, in which indole-3-acetic acid signaling is constitutively activated. Our original hypothesis placed cell-wall modifications at the heart of the wat1 resistance phenotype. However, the results presented here suggest a mechanism involving root-localized metabolic channeling away from indole metabolites to SA as a central feature of wat1 resistance to R. solanacearum